Building Brain-Inspired Information Networks
Main Lab Location:
Osaka Univ. (Suita Campus)
Graduate School of Information Science and Technology
1-5, Yamadaoka, Suita, Osaka 565-0871, Japan
for detailed researh contributions and other topics.
My research includes future networking technologies inspired by self-organization in biological systems – especially network design and control.
The dynamic nature of new infrastructures such as wireless networks, mobile ad-hoc networks, sensor networks and Internet of Things (IoT), as well as application-level network services like P2P (peer-to-peer) networks, requires a fundamentally new approach for the future network control, management, and planning. Biological systems are vastly more adaptable and robust because of their self-organsing nature, and we think this principle of self-organisation is the key to future network design and control.
For example, we have been developing network control based on the attractor selection principle, or Yuragi (fluctuation in Japanese) principle, which derives from intra-cellular signaling dynamics. This has been successfully applied to several network control problems, including mobile ad-hoc routing, virtual topology control of the IP over WDM network, and media selection in cognitive networks. This achieves adaptable and robust control against dynamically changing environments without a priori knowledge and preprogrammed adaptation rules.
Internet-based networks are growing rapidly, for example with the increase in mobile users and sensor nodes as in M2M (machine to machine communication) and IoT (Internet of Things). Network virtualization is another driver to increase the number of (virtualized) networks and nodes. More recently, research on CCN (content-centric networking) has started to build an infrastructure of future networks. CCN is introducing an increase of management complexity within the network. Such networks cannot be designed using traditional approaches where the composition of the entire system has been analyzed first and each component is then designed. Instead, we highly expect that principles of biological systems will be able to establish completely new methods allowing adaptability (to new environments and services), predictability (to uncertain environments), and evolvability (to unpredictable growth).
Leibnitz, K., Murata, M. Attractor selection and perturbation for robust networks in fluctuating environments. IEEE Network, Special Issue on Biologically Inspired Networking, vol. 24, no. 3, pp. 14-18, May/June 2010.
Koizumi, Y., Miyamura, T., Arakawa, S., Oki, E., Shiomoto, K., Murata, M. Adaptive virtual network topology control based on attractor selection," IEEE/OSA Journal of Lightwave Technology, vol. 28, pp. 1720-1731, June 2010.
Announcements / News:
Post-doc positions may be available. Please feel free to contact me at anytime if you have an interest.